Rešenje su opet našli genetski algoritmi. Prostom mutacijom i selekcijom na kodu koji organizuje hodanje, evoluirali su prvo jednostavni. Taj način se zasniva na takozvanim genetskim algoritmima, koji su zasnovani na principu evolucije. Genetski algoritmi funkcionišu po veoma jednostavnom. Transcript of Genetski algoritmi u rješavanju optimizcionih problme. Genetski algoritmi u rješavanju optimizacionih problema. Full transcript.

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Pogledajmo tri primera ovakvih algoritama. Tradicionalne antene zahtevaju kvadrifilarni heliks, i nisu ni blizu dovoljno osetljive. Deo ove efikasnosti je molekularan, zasnovan na strukturi i funkciji hlorofila. Ali postoje i drugi nivoi optimizacije: Ali, u stvari, radi se o veoma komplikovanom procesu: Odakle, onda, te informacije? Morale su nekako da nastanu. Genetic algorithms—do they show that evolution works? The crucial issue the genetsi of information. Spetner shows that time and chance cannot produce new more genetic information.

A genetic algorithm GA is a computer program that supposedly simulates biological evolution. GAs have found limited application in generating novel engineering solutions—for example, an electronic circuit that filters out a geenetski frequency. Because of this, some apologists for evolution claim that these programs show that biological evolution can create the information needed to proceed from less complex to more complex organisms i.

However, GAs do not mimic or simulate biological evolution because with a GA: Many biological traits are qualitative—it either works or it does not, so there is no step-wise means of getting from no function to the function. A GA can only select for a very limited number of traits. Even with the simplest bacteria, which are not at all simple, hundreds of traits have to be present for it to be viable survive ; selection has to operate on all traits that affect survival.

Something always survives to carry on the process.

There is no rule in evolution that says that some organism s in the evolving population will remain viable no matter what mutations occur. In fact, the GAs that I have looked at artificially preserve the best of the previous generation and protect it from mutations or recombination in case nothing better is produced in the next iteration. This has a ratchet effect that ensures that the GA will generate the desired outcome—any move in the right direction is protected.

In the real world, selection coefficients of 0. Bacteria can only double their numbers per generation. For example, if a population of 1, bacteria had only one survivor alboritmithen it would take 10 generations to get back to 1, Generation time is ignored. A generation can happen in a computer genetsko microseconds whereas even the best bacteria take about 20 minutes.

Multicellular organisms have far longer generation times. The mutation rate is artificially high by many orders of magnitude.

Such mutation rates in real organisms would result in all the offspring being non-viable error catastrophe. This is why living things have exquisitely designed editing machinery to minimize copying errors to a rate of about one in a billion per cell division. The smallest real world genome is over 0. This is equivalent to over a million bits of information.

Even if a GA generated bits of real information, as one of the commonly-touted ones claims, that is equivalent to maybe one small enzyme—and that was achieved with totally artificial mutation rates, generation times, selection coefficients, etc. This is pointed out in more detail by biophysicist Dr Lee Spetner in his refutation of a skeptic. In real organisms, mutations occur throughout the genome, not just in a gene or section that specifies a given trait. This means that all the deleterious changes to other traits have to be eliminated along with selecting for the rare desirable changes in the trait being selected for.


This is ignored in GAs. With genetic algorithms, the program itself is protected from mutations; only target sequences are mutated. Indeed, if it were not quarantined from mutations, the program would very quickly crash. However, the reproduction machinery of an organism is not protected from mutations. Many biological traits require many different components to be present, functioning together, for the trait to exist at all e.

Polygeny where a trait is determined by the combined action of more than one gene and pleiotropy where one gene can affect several different traits are ignored.

Furthermore, recessive genes are ignored recessive genes cannot be selected for unless present as a pair; i. Haldane pointed out that, algorifmi on the theorems of population genetics, there has not been enough time for the geentski organisms with low reproductive rates and long generation times to genetsmi.

Multiple coding genes are ignored. From the human genome project, it appears that, on average, each gene codes for at least three different proteins see Genome Mania — Deciphering the human genome. Creating a GA to generate such information-dense coding would seem to be out of the question. Such demands an intelligence vastly superior to human beings for its creation. Evolution is by definition purposeless, so no computer program that has a pre-determined goal can simulate it—period.

That GAs are not valid simulations of evolution because of this fundamental problem has been acknowledged—see this quote. Of course that is impossible as is evolution.

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A number of modules or subroutines are normally specified in the program, and the ways these can interact is also specified. The GA program finds the best combinations of modules and the best ways of interacting them. The amount of new information generated is usually quite trivial, even with all the artificial constraints designed to make the GA work.

In the Beginning Was Information by Dr. Werner Gitt This book discusses the origin of life from the viewpoint of information science with many striking examples to clarify the following questions—What is the origin of information? What are the laws of nature about information? How did language and communication develop? Is artificial intelligence possible? For the above reasons and some of them overlapand no doubt there are more that could be added, GAs do not validate biological evolution.

It does not take long with a decent calculator to see that the information space available for a minimal real world organism of just several hundred proteins is so huge that no naturalistic iterative real world process could have accounted for it—or even the development of one new protein with a fundamentally new function. This exercise has led to grandstanding by some evolutionists that this proves creationists wrong.

However, many of the same problems outlined above also apply to this programming exercise. For example, the selection coefficient is extremely high, the genome is extremely small, the mutation rate high, no possibility of extinction is permitted, etc.

For many other problems, see the critique by Dr Royal Truman. As Spetner says, look, if mutations and natural selection have generated all the information we see, then we should be able to easily find some examples of some new information i.

The best that anyone has come up with is a GA, which does not simulate real world evolution, for the reasons outlined above.

Double headed arrows denote pistons which extend and retract alternately, providing motion. Are these computer exercises relevant to biological evolution? Scientists and engineers have used computers to optimize structures and equations for many years, by getting the computer to change the values of some coefficients slightly and then test to see if the result is closer to the algogitmi outcome.


If it is, then the coefficients are changed again and the outcome is tested again. If not, then go back and try varying the coefficients in a different direction and test again. Many thousands of such cycles can produce the desired outcome that would be too time-consuming and tedious to find by manual techniques.

The only variation is basically that, with genetic algorithms, a number of models are generated in parallel and tested, with a proportion of the best being selected likened to natural selection for further iterations. Such computer simulations are altoritmi confined to a limited number of components.

Genetski algoritmi u rješavanju optimizcionih problme by Jovana Janković on Prezi

For example, in the current example, the maximum number of components seems to be about The number of critical components—that is, those necessary for the robot to function—is only about 4 or 5 parts. Real organisms have many thousands of different components. The rods and pistons are joined or not joined at their ends by ball-joints.

The lengths are varied one at a time in small increments. In the real world, even the simplest bacterium has hundreds of thousands of sites where mutations can occur. This is a fundamental problem with the evolutionary story for living things—mutations cause the destruction of the alhoritmi information and consequently they are known by the thousands of diseases they causenot its creation. In the real world of living organisms, selection must be for hundreds of different traits at once.

For every mutation that might affect a trait such as movement, hundreds of mutations will affect other traits, such as reproduction, metabolism of sugars, etc. Inclusion of many traits in the computer program would render the procedure unworkable it is very difficult to get iterative processes to work with more than one goal.

The alyoritmi exercise did not start with nothing—it started with a program generated by intelligent scientists that specified the way in which the robots could be constructed The programmer has pre-programmed the computer for a specific goal. The computer exercise did not start with nothing—it started with a program generated by intelligent scientists that specified the way in which the robots could be constructed. Given the components pistons, rods, etc. They are dependent on their human creators to manufacture them.

The simplest of living things can gather the raw materials and and energy to manufacture the components to reproduce themselves. Even atheists like Richard Dawkins admit that algorimti things look like genetwki are beautifully designed—they look like an intelligent creator cleverly designed them and then he uses evolutionary story-telling to try to explain how they actually made themselves by mutations and natural selection.

The arrangement of the parts looks like the result of a haphazard process. Living things do not look like they came about by a haphazard random process. They look like they were designed.

Real-world organisms need to be viable and maintain viability. ReMine addresses the problems of mutation rates and selection coefficients for the evolutionary story, showing that the neo-Darwinian mechanism just cannot explain the amount of information in genomes.

In fact the severe limitations on such procedures, even with fast, powerful modern computers, shows how real-world biological molecules-to-man evolution is impossible, even if there were the eons of time claimed by evolutionists. Computational methods often employ genetic algorithms GAs. The appeal of GAs is that they are modeled after biological evolution.